IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning
Open accessAbstract
Slowly but steadily, the Internet of Things (IoT) is becoming more and more ubiquitous in our daily life. However, it also brings important security and privacy challenges along with it, especially in a sensitive context such as the smart home. In this position paper, we propose a novel architecture for smart home, called our, focusing on the security and privacy aspects, which combines federated learning with secure data aggregation. We hope that our proposition will provide a step forward towards achieving more security and privacy in smart homes.
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FL helps users build home assistant solutions such as object detection and control their home by coordination of distributed IoT devices in a privacy-preserving manner.
“In this way, FL helps users (i.e., smart home owners) to build home assistant solutions such as object detection and control their home by the coordination of distributed IoT devices in a privacy-preserving manner.”
From Federated Learning for Internet of Things: A Comprehensive Survey (2021) · cites this paper as [182]
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Federated Learning for Internet of Things: A Comprehensive Survey
Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Vincent Poor, H. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3075439 · Cited by 1,503 · see citing works
“example, the work in [182] implements a testbed for an FL-”
MethodA review of applications in federated learning
Li, L., Fan, Y., Tse, M., & Lin, K.-Y. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106854 · Cited by 1,559 · see citing works
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
Federated learning review: Fundamentals, enabling technologies, and future applications
Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556 · see citing works
SCA: Sybil-Based Collusion Attacks of IIoT Data Poisoning in Federated Learning
Xiao, X., Tang, Z., Li, C., Xiao, B., & Li, K. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3172310 · Cited by 99 · see citing works
- Authors
- Aivodji, U. M., Gambs, S., & Martin, A.
- Affiliations
- Université du Québec à Montréal (CA)
- ORCID
- Martin, Alexandre
- Venue
- 2019 IEEE Security and Privacy Workshops (SPW)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 175-180
- OpenAlex
- W2974306260
- Cited by
- 93
- References
- 0
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 22 · 2023: 15 · 2022: 21 · 2021: 12 · 2020: 8
- FWCI
- 3.92
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Security and Privacy Workshops (SPW) (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Keywords
- Internet of Things, home automation, federated learning, privacy preservation, Internet of Things security, secure aggregation, smart home architecture
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